Cluster Tracking with Time-of-Flight Cameras

Dan Witzner Hansen; Mads Hansen; Martin Kirschmeyer; Rasmus Larsen; Davide Silvestre

doi:10.1109/CVPRW.2008.4563156

Cluster Tracking with Time-of-Flight Cameras

Dan Witzner Hansen, Mads Hansen, Martin Kirschmeyer, Rasmus Larsen, Davide Silvestre

Technical University of Denmark

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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Abstract

We describe a method for tracking people using a time-of-flight camera and apply the method for persistent authentication in a smart-environment. A background model is built by fusing information from intensity and depth images. While a geometric constraint is employed to improve pixel cluster coherence and reducing the influence of noise, the EM algorithm (expectation maximization) is used for tracking moving clusters of pixels significantly different from the background model. Each cluster is defined through a statistical model of points on the ground plane. We show the benefits of the time-of-flight principles for people tracking but also their current limitations.

Original language	English
Title of host publication	Computer Vision and Pattern Recognition Workshops : Time-of-flight based computer vision
Publisher	IEEE Computer Society Press
Publication date	2008
Pages	1-6
ISBN (Print)	978-1-4244-2339-2
DOIs	https://doi.org/10.1109/CVPRW.2008.4563156
Publication status	Published - 2008
Event	2008 IEEE Conference on Computer Vision and Pattern Recognition - Anchorage, United States Duration: 23 Jun 2008 → 28 Jun 2008 http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4558014

Conference

Conference	2008 IEEE Conference on Computer Vision and Pattern Recognition
Country/Territory	United States
City	Anchorage
Period	23/06/2008 → 28/06/2008
Internet address	http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4558014

Bibliographical note

Copyright: 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

Keywords

computer vision
action tracking
time-of-flight camera

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10.1109/CVPRW.2008.4563156

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title = "Cluster Tracking with Time-of-Flight Cameras",

abstract = "We describe a method for tracking people using a time-of-flight camera and apply the method for persistent authentication in a smart-environment. A background model is built by fusing information from intensity and depth images. While a geometric constraint is employed to improve pixel cluster coherence and reducing the influence of noise, the EM algorithm (expectation maximization) is used for tracking moving clusters of pixels significantly different from the background model. Each cluster is defined through a statistical model of points on the ground plane. We show the benefits of the time-of-flight principles for people tracking but also their current limitations.",

keywords = "computer vision, action tracking, time-of-flight camera",

author = "Hansen, {Dan Witzner} and Mads Hansen and Martin Kirschmeyer and Rasmus Larsen and Davide Silvestre",

note = "Copyright: 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE; 2008 IEEE Conference on Computer Vision and Pattern Recognition, CVPR2008 ; Conference date: 23-06-2008 Through 28-06-2008",

year = "2008",

doi = "10.1109/CVPRW.2008.4563156",

language = "English",

isbn = "978-1-4244-2339-2",

pages = "1--6",

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}

Hansen, DW, Hansen, M, Kirschmeyer, M, Larsen, R & Silvestre, D 2008, Cluster Tracking with Time-of-Flight Cameras. in Computer Vision and Pattern Recognition Workshops: Time-of-flight based computer vision. IEEE Computer Society Press, pp. 1-6, 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska, United States, 23/06/2008. https://doi.org/10.1109/CVPRW.2008.4563156

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T1 - Cluster Tracking with Time-of-Flight Cameras

AU - Hansen, Dan Witzner

AU - Hansen, Mads

AU - Kirschmeyer, Martin

AU - Larsen, Rasmus

AU - Silvestre, Davide

N1 - Copyright: 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

PY - 2008

Y1 - 2008

N2 - We describe a method for tracking people using a time-of-flight camera and apply the method for persistent authentication in a smart-environment. A background model is built by fusing information from intensity and depth images. While a geometric constraint is employed to improve pixel cluster coherence and reducing the influence of noise, the EM algorithm (expectation maximization) is used for tracking moving clusters of pixels significantly different from the background model. Each cluster is defined through a statistical model of points on the ground plane. We show the benefits of the time-of-flight principles for people tracking but also their current limitations.

AB - We describe a method for tracking people using a time-of-flight camera and apply the method for persistent authentication in a smart-environment. A background model is built by fusing information from intensity and depth images. While a geometric constraint is employed to improve pixel cluster coherence and reducing the influence of noise, the EM algorithm (expectation maximization) is used for tracking moving clusters of pixels significantly different from the background model. Each cluster is defined through a statistical model of points on the ground plane. We show the benefits of the time-of-flight principles for people tracking but also their current limitations.

KW - computer vision

KW - action tracking

KW - time-of-flight camera

U2 - 10.1109/CVPRW.2008.4563156

DO - 10.1109/CVPRW.2008.4563156

M3 - Article in proceedings

SN - 978-1-4244-2339-2

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EP - 6

BT - Computer Vision and Pattern Recognition Workshops

PB - IEEE Computer Society Press

T2 - 2008 IEEE Conference on Computer Vision and Pattern Recognition

Y2 - 23 June 2008 through 28 June 2008

ER -

Cluster Tracking with Time-of-Flight Cameras

Abstract

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Bibliographical note

Keywords

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